The present invention is directed generally to systems that perform both control and diagnostics operations on a monitored system, and more specifically, a system in which the diagnostics system is integrated with the control system to provide optimum control and health assessment of the monitored system based on the output of both systems.
Known control systems typically are feed-forward or feedback systems, which implement closed-loop control to obtain and maintain certain operating conditions. Conventionally, control systems are often used to maintain a prescribed controlled system operating state or condition such as temperature, speed, position, trajectory, torque, etc., or to achieve a prescribed system state such as for motion control and robotics applications. These systems typically implement a stable control law that works to maintain operating performance notwithstanding certain operational and/or physical constraints and external disturbances. Moreover, many of these systems exhibit non-linear characteristics. For example, servo actuator systems have inherent non-linear characteristics. Control design typically requires that a nonlinear system be treated as linear, usually by canceling the nonlinearity using feedback. These xe2x80x9clinearizingxe2x80x9d techniques have not found wide acceptance because they are generally not robust enough to operate in real-world applications. Often they are infeasible due to the large number of control inputs required to regulate the system. Consequently, these techniques have had relatively little use in solving real-world problems. For instance, the non-linearities associated with the aforementioned servo actuator systems cannot be ignored in control design. As such, a control system is needed that accounts for system nonlinearities using an alternative to correcting for such nonlinearlities with xe2x80x9clinearizingxe2x80x9d techniques.
Some known control systems utilize non-linear control methods such as model-reference (MRAC), gain scheduling, controller scheduling, fuzzy logic, or feedback linearization, to dynamically modify the operation of the controller in response to sensed changes in system behavior. Such changes in behavior include different system dynamics including compliance, noise or other related changes. Moreover, such systems may be time varying and may be difficult or impossible to model for control purposes. Notably, these control systems typically operate in isolation from any diagnostic or prognostic systems.
In addition to providing control, some systems implement independent diagnostics apparatus to monitor the overall health of either the apparatus being controlled, or the control system itself. Some systems may have no control, but only machinery diagnostics capabilities. Notably, assessing system health can be used to minimize unscheduled system downtime and to prevent equipment failure. This capability can avoid a potentially dangerous situation caused by the unexpected outage or catastrophic failure of machinery. Moreover, some diagnostic systems inconveniently require an operator to manually collect data from machinery using portable, hand-held data acquisition probes.
Other known systems have sensors and data acquisition and network equipment permanently attached to critical machinery for remote diagnostics. Typically the diagnostics equipment is directed to detecting problems with the control system hardware itself or monitoring the integrity of the output, i.e., monitoring when the control system response is outside prescribed time or value limits. As noted above, control system health monitoring, health assessment and prognostics generally are performed in isolation from any associated control system. These systems typically conduct passive monitoring and assess system health using diagnostic algorithms and sensors dedicated to establish system health. This passive monitoring is frequently done using off-line, batch-mode data acquisition and analysis to establish the health of the system.
For example, in FIG. 1, a conventional prior art automated control and diagnostics monitoring system 10 for use with a machine 12 that operates a plant (or as part of a process) is shown. System 10 includes a control module 14 that provides closed loop feedback control of machine 12 to maintain a set point condition (e.g., a velocity). In addition, system 10 includes a diagnostics block 16 electrically coupled to machine 12 for monitoring the health of the machine. In particular, diagnostics block 16 receives sampled systems data and processes the data to assess the health of the machine 12. A primary drawback of such a system is that diagnostics block 16 operates independent and isolated from control module 14 and performs off-line diagnostic processing which is not readily adaptable to integration with on-line control.
However, as noted previously, because virtually all diagnostics systems perform off-line diagnostic processing, it has been extremely difficult to implement diagnostics processing real-time in coordination with on-line control. Presently, no system exists which integrates control and diagnostics to optimize control outputs dynamically in real-time.
As a result, the art of control and diagnostics systems is in need of a control and diagnostics system that advantageously utilizes the outputs of each system to optimize the performance of both systems. Such a system would be able to dynamically optimize the operation of the controlled system by accurately diagnosing problems and predicting the future state of the controlled system based on health data from diagnostic sensors and/or from the control system. This would enable the system to alter the control operation in a goal-directed manner to facilitate diagnostics and prognostics, to reduce or eliminate excessive wear or degradation of the controlled system, or to achieve other operational objectives.
Notably, it has been determined that the information developed through the use of a diagnostics system is particularly valuable in assessing what type of control action should be applied to the monitored system. Vice versa, the output of the control system, which is based on controlled system response, is valuable in determining the overall health of the controlled system.
The preferred embodiment overcomes the limitations associated with prior systems which perform control and diagnostics operations independently on a controlled unit by utilizing advances in machinery diagnostics/prognostics in conjunction with conventional control hardware and non-linear/adaptive control techniques to provide a compact, cost-effective and intelligent system. The system of the preferred embodiment provides a tight coupling of embedded hybrid diagnostics and control to achieve optimum system performance in conjunction with reliable prognostics to facilitate maximizing machinery longevity and lowest cost of ownership. The integrated diagnostics and control elements of the present system allow efficient operation of the controlled system over its lifetime by intelligently predicting the time-life trajectory of the controlled system and altering operation accordingly.
The present invention readily utilizes existing architectures such as integrated motor-drive and motor-drive actuator systems which provide further enhanced operation by extending the life of these controllers with the use of model-based and qualitative/causal model information to provide intelligent control and diagnostics. More particularly, the model-based diagnostics approach of the present invention allows integration of the control algorithms with diagnostics algorithms to intelligently trade off optimizing performance to avert or accommodate failures, and to meet demanding performance requirements in a wide range of application environments. Overall, the result of implementing these features is a coherent, coupled control and diagnostics system that outperforms known systems having independent diagnostics and control apparatus operating in isolation.
According to a preferred embodiment, an integrated control and diagnostics system for a controlled unit includes a diagnostics module integrated with the motor that generates a diagnostics information signal indicative of the health of the motor. In addition, the system includes a controller integrated into the motor. To optimize operation, the diagnostics information signal is used to modify the control provided by the controller, as required. Moreover, the output of the control module is coupled to the diagnostics module so that the health assessment made by the diagnostics module can be based at least in part on the output of the controller and the systems response to this control action.
According to another aspect of the invention, the controller is associated with at least one changeable parameter, the parameter being changeable in response to the diagnostics information signal. Moreover, the controller preferably includes a velocity feedback loop and a torque feedback loop to implement the control.
According to yet a further aspect of the preferred embodiment, the integrated control and diagnostics system includes an enhancement module that generates an evolving set of design rules based on a plurality of the diagnostics information signals so as to facilitate designing an improved version of the motor and drive system. The enhancement module preferably includes a memory having a model embedded therein to generate the evolving set of design rules according to user specifications.
According to another aspect of the invention, the method of optimizing control and diagnostics operations performed on a controlled unit includes the steps of providing a diagnostics module and a control module, each of which is integrated with the controlled unit. In addition, the method includes the step of generating a control signal with a control module in response to feedback from the controlled unit and generating a diagnostics signal indicative of the health of the controlled unit, with the diagnostics module, based on the control signal. Also, the method includes the step of predicting when a controlled unit failure will occur, as well as the cause of the controlled unit failure. Finally, the method includes the step of determining whether to alter the control signal based on the predicting steps.
According to yet a further aspect of the preferred embodiment, a method of optimizing the useful life of a motor according to a preventive maintenance schedule that includes a plurality of preventive maintenance checkpoints includes the step of sensing a motor parameter during operation of the motor. Thereafter, the method includes generating a control signal with a controller based on the motor parameter and then diagnosing a health condition of the motor based on at least one of an operating objective, the control signal and a process constraint. The method also predicts, based on the health condition, whether a motor fault condition will occur prior to the next preventive maintenance checkpoint and then determines whether to alter the control signal in response to the prediction. If the altered control signal is then issued, the method will again determine if a motor fault will occur under the new control scheme prior to the preventive maintenance checkpoint, and then prescribe any necessary change in the control.
These and other objects, features, and advantages of the present invention will become apparent to those skilled in the art from the following detailed description and drawings. It should be understood, however, that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the present invention without departing from the spirit thereof, and the invention includes all such modifications.